A hybrid music recommendation technique that utilizes a Bayesian network called a three-way aspect model is disclosed in a paper titled “A Hybrid Music Recommender System based on Probabilistic Integration of User Ratings and Acoustic Features” (Non-Patent Document 1) presented in a collection of papers published in a research report meeting SIGMUS 66 held on Aug. 7, 2006 by the Information Processing Society of Japan. The known recommendation technique utilizes both “collaborative filtering” and “content-based filtering”. The “collaborative filtering” technique recommends musical pieces to a certain user by considering ratings of musical pieces provided by other users. For example, in the case where a recommendation is made to a user that likes musical pieces A and B, a musical piece C is recommended to the user if there are many other users that like the musical pieces A, B, and C. However, the technique involves the following two issues:
(1) Unrated musical pieces (new or minor musical pieces) cannot be recommended.
(2) The recommended musical pieces are often well-known hit tunes, which leads to poor variations.
The “content-based filtering” technique recommends musical pieces that are similar to favorite musical pieces of a user by automatically extracting musical elements such as a genre or topic, a musical instrument arrangement, and acoustic features from audio signals. However, the technique involves the following two issues:
(1) The recommendation accuracy is low because the technique for automatically extracting musical elements is still in the research stage.
(2) Information that cannot be obtained from the audio signal but that is useful for recommendation (such as popularity and cultural background) is not considered.
The hybrid music recommendation technique has been developed in consideration of the issues of the two techniques. In the technique, a recommendation is made by considering rating information and content-based information at the same time to address the issues of the recommendation techniques according to the related art and improve the recommendation accuracy.
Specifically, the technique employs a scheme for probabilistically integrating “5-scale rating scores or rating histories given by users” and “acoustic features automatically extracted from audio signals” using a Bayesian network model (three-way aspect model). The Bayesian network model (three-way aspect model) is described in a paper titled “Probabilistic Models for Unified Collaborative and Content-based Recommendation in Sparse-data Environments” (Non-Patent Document 2) presented by A. Popescul, L. Ungar, D. Pennock, and S. Lawrence in UAI, 2001, pp. 437-444. The model directly models the musical preference of a user by expressing “conceptual topics, which are not directly observable, as “latent variables”. Therefore, the generation process of observation data (musical piece ratings and acoustic features) can be naturally expressed. The probability values of respective branches connecting four nodes of the model are calculated using a maximum likelihood estimation method based on an EM algorithm. The integration scheme is found to be theoretical and highly reliable.
[Non-Patent Document 1] A paper titled “A Hybrid Music Recommender System based on Probabilistic Integration of User Ratings and Acoustic Features” presented in a collection of papers published in a research report meeting SIGMUS 66 held on Aug. 7, 2006
[Non-Patent Document 2] A paper titled “Probabilistic Models for Unified Collaborative and Content-based Recommendation in Sparse-data Environments” presented by A. Popescul, L. Ungar, D. Pennock, and S. Lawrence in UAI, 2001, pp. 437-444